Lisa Sikkema
PHD Student at Helmholtz Munich
Munich, Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolLisa Sikkema is a PhD student at Helmholtz Munich specializing in machine learning for single-cell biology, with eight years of experience bridging computational methods and clinical collaboration. She has built and evaluated large-scale integration and transfer-learning models on resources like the Human Lung Cell Atlas and developed patient representation and disease classification methods used in consortium projects. Previously at Memorial Sloan Kettering she designed reusable scRNA-seq pipelines and analyzed diverse clinical datasets, gaining hands-on expertise with sparse, high-dimensional single-cell data. Lisa contributes to community resources such as the Single-Cell Best Practices book, authoring tutorials on cell-type annotation that prioritize practical, reproducible code and marker-gene interpretation. Her background spans rigorous quantitative training (PhD in ML & Medical Biology, MSc in Oncology) and interdisciplinary work with clinicians and biologists, enabling translational impact. Colleagues note she pairs strong methodological rigor with a knack for making complex analyses accessible through teaching and mentoring.
8 years of coding experience
Master of Science - MS, Oncology, 8.9/10, Master of Science - MS, Oncology, 8.9/10 at Vrije Universiteit Amsterdam (VU Amsterdam)Doctor of Philosophy - PhD, Machine Learning and Medical Biology, Doctor of Philosophy - PhD, Machine Learning and Medical Biology at Technical University of MunichBachelor of Arts - BA, Philosophy, 8.6, Bachelor of Arts - BA, Philosophy, 8.6 at University of Amsterdam
Github Skills (10)
cell10
rna-seq10
user-manual10
seq10
jupyter-notebook10
sc10
data-analysis10
python9
scanpy9
pandas8
Programming languages (7)
Github contributions (5)
theislab/single-cell-best-practices
Jul 2022 - Dec 2022
https://www.sc-best-practices.org
Role in this project:
Data Scientist
Data ScientistContributions:4 reviews, 20 commits, 3 PRs in 5 months
Contributions summary:Lisa is contributing to the annotation chapter of a best-practices book, focusing on single-cell RNA-seq data analysis. They are developing a tutorial on cell-type annotation, covering manual and automated annotation methods, with an emphasis on marker gene expression. The user is implementing code examples and providing explanations, with the aim of helping readers better understand and utilize the data. The commits demonstrate a focus on practical application and educational content related to scRNA-seq analysis.
in-progresssingle-cellwork-in-progressrna-seq
LungCellAtlas/HLCA
Mar 2022 - Jul 2022
Contributions:15 commits, 1 PR, 20 pushes in 4 months
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